Artificial Intelligence Analysis of Periorbital Rejuvenation

Abstract Background Periorbital rejuvenation surgery aims to restore a youthful appearance to the face. Despite the popularity of these procedures, few objective measurements exist to evaluate their impact on perceived facial aging. Objectives This study aims to quantify the impact of brow lift and blepharoplasty on age as perceived by convolutional neural network (CNN) algorithms. Methods A retrospective review was performed on patients who underwent upper blepharoplasty, lower blepharoplasty, and/or brow lift at a single cosmetic practice between 2018 and 2023. Collected data included patient demographics, procedure performed, fat pad resection, and pre- and postoperative frontal images. Each photo was analyzed by four artificial intelligence (AI) platforms to estimate the change in perceived age following surgery. The estimated age reduction was compared between procedures. Results Of the 153 included patients, 118 underwent blepharoplasty, 12 underwent brow lift, and 23 had both blepharoplasty and brow lift. Across all AI platforms, the mean age estimation percent error was 10.6%, with a tendency for AI to underestimate compared to true age. Univariate analysis revealed an age reduction following any surgery of 1.03 years (p<0.001). When controlling for other variables, brow lift patients saw a mean age reduction of 1.432 years (p=0.031). Upper and lower blepharoplasty, patient characteristics, and ancillary procedures were not found to be independently associated with significant age reduction. Conclusions Brow lifts provide significant reduction in perceived age. When planning for periorbital rejuvenation, a thorough preoperative evaluation should be performed, and additional consideration should be given to brow lifting procedures.